Sr Advisory Software Engineer

Job Summary

Join Pitney Bowes as Senior Advisory Software Engineer Years of Experience: 8-11 years Job Location- Noida

Responsibilities

• Define and drive enterprise data architecture for large-scale cloud-based analytics platforms.
• Design and build scalable, resilient, and high-performance data pipelines using Snowflake and AWS services.
• Lead architecture discussions, solution design, and technical decision-making for data engineering initiatives.
• Design and implement end-to-end ETL/ELT pipelines for structured, semi-structured, and streaming data.
• Develop robust ingestion frameworks using Snowpipe, COPY INTO, Streams, Tasks, Dynamic Tables, and Stored Procedures.
• Design efficient dimensional and analytical data models to support reporting, analytics, and downstream applications.
• Optimize Snowflake performance through query tuning, warehouse sizing, clustering strategies, materialized views, and caching techniques.
• Drive cloud cost optimization by implementing efficient warehouse management, storage lifecycle policies, and workload optimization.
• Design real-time streaming architecture using AWS Kinesis, Lambda, and event-driven processing patterns.
• Implement enterprise-grade data governance including RBAC, masking policies, row-level security, auditing, and regulatory compliance.
• Design scalable and secure data-sharing solutions across business units and external partners.
• Monitor and troubleshoot production data pipelines using observability tools, logs, metrics, and data quality platforms.
• Perform production triage, root cause analysis, and ensure timely resolution of critical data platform issues.
• Create and maintain architecture artifacts including data flow diagrams, logical and physical data models, UML diagrams, and HLD/LLD documentation.
• Collaborate with DevOps teams to automate CI/CD pipelines and infrastructure deployment for data platforms.
• Mentor data engineers, conduct design reviews, and promote engineering best practices across teams.
• Ensure data platform reliability, scalability, security, and operational excellence.
• Work in an Agile environment with ownership of end-to-end delivery.
• Participate in production support and incident management when required.

Qualifications Skills

The role requires a talented, self-directed, and self-motivated individual with a strong work ethic and the following qualifications, experience, and skills:

• 9+ years of experience in Data Engineering, Data Warehousing, or Data Platform development.
• 7+ years of experience designing enterprise-scale data architectures.
• Deep expertise in Snowflake including Snow pipe, COPY INTO, Streams, Tasks, Dynamic Tables, Stored Procedures
• Query optimization using Query Profile, EXPLAIN plans, and Query History
• Warehouse sizing, clustering keys, materialized views, and result caching
• Time Travel, Fail-safe, Data Sharing, Snowflake Marketplace
• ACCOUNT_USAGE and INFORMATION_SCHEMA monitoring
• Strong experience in dimensional data modeling, star/snowflake schemas, and semi-structured data (VARIANT, FLATTEN, PARSE_JSON).
• Hands-on experience with AWS services including S3, Kinesis Data Streams, Kinesis Firehose, Lambda, IAM, and CloudWatch.
• Experience designing real-time and event-driven data processing pipelines.
• Strong expertise in production troubleshooting, performance tuning, and root cause analysis.
• Experience with data observability platforms such as Monte Carlo.
• Experience using Sumo Logic for log aggregation, monitoring, dashboarding, and troubleshooting.
• Experience developing and maintaining integration pipelines using SnapLogic.
• Strong working knowledge of MongoDB includes schema design, indexing strategies, aggregation framework, and query optimization.
• Experience implementing RBAC, data masking, row-level security, and enterprise data governance.
• Proficiency with Git, CI/CD pipelines, and infrastructure automation.
• Strong understanding of distributed systems, cloud-native architectures, and scalable data platforms.
• Ability to create architecture documentation including HLD, LLD, UML, and data flow diagrams.
• Experience handling production support, incident management, and SLA-driven environments.
• Familiarity with Agile and Scrum methodologies.
• Excellent analytical, problem-solving, and communication skills.
• Ability to work independently in a fast-paced, matrixed organization.

Good to Have

• Experience with Python for data engineering and automation.
• Knowledge of Data Mesh, Data Fabric, and modern data architecture patterns.
• Experience with enterprise metadata management, data cataloging, and lineage tools.
• Knowledge of data security, privacy regulations, and governance frameworks.
• Experience modernizing legacy data platforms and migrating workloads to Snowflake.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.